AI Engineer (Agentic AI & ML)

Avenga is an international engineering firm helping businesses operate with AI at the core. With 6,000+ experts worldwide, we combine engineering expertise with AI-native thinking to turn ambitious ideas into real-world impact across industries, technologies, and markets.

We're looking for a Senior AI Engineer / ML Engineer to join an R&D team building next-generation AI solutions based on Large Language Models and agentic AI.

This is not a traditional ML engineering role. You'll work on rapidly evolving AI products, combining modern LLM capabilities with strong Machine Learning fundamentals to design production-ready solutions. You'll be expected to evaluate business problems, choose appropriate ML approaches, architect end-to-end systems, and independently drive ideas from concept to deployment.

If you enjoy solving complex engineering problems around LLMs and agentic AI, experimenting with new technologies, and turning research into practical products, we'd love to hear from you.

You'll join a senior AI engineering team working in a fast-moving R&D environment where experimentation, ownership, and rapid iteration are part of everyday work.

The team designs and delivers production AI systems based on LLMs, agentic workflows, and modern Machine Learning techniques. Engineers are expected to take ownership of technical decisions, collaborate closely with architects and product stakeholders, and continuously evaluate new approaches as the technology evolves.

This role offers the opportunity to work on cutting-edge AI products while having a real impact on both technical direction and product design.

What supports your journey

Through our values, we strive to provide you with the tools, autonomy, trust, and assistance you need to excel. Enjoy benefits like private health insurance, well-being programs, flexible and hybrid work models, laptops and gear, training, language classes, social events, great offices, and more.

People are at the core of Avenga. We provide equal opportunities regardless of race, ethnicity, gender identity, sexual orientation, disability, age, religion, or any other characteristic. We are committed to a respectful environment where everyone can be themselves, share their ideas, and feel that they belong.

• 6+ years of commercial experience building Machine Learning systems.
• Proven ML technical leadership experience — the ability to understand business problems, select appropriate ML approaches, design end-to-end solutions, and make technical decisions beyond simply implementing models.
• Strong hands-on experience with agentic orchestration frameworks such as LangGraph, LangChain, or similar.
• Experience designing multi-agent systems, orchestration workflows, state management, tool calling, and structured outputs.
• Hands-on experience building production-grade applications using Large Language Models (OpenAI, Gemini, Anthropic or similar).
• Strong understanding of:
• Retrieval-Augmented Generation (RAG)
• embeddings
• semantic search
• retrieval optimization
• prompt engineering and context management
• Experience working with vector databases (Vertex AI RAG Engine, Pinecone, FAISS, Weaviate or similar).
• Strong Python development skills.
• Experience building APIs with FastAPI (or similar frameworks).
• Experience with async programming and long-running backend processes.
• Experience designing structured data models using Pydantic (or similar validation frameworks).
• Experience with Docker.
• Comfortable working independently in fast-changing R&D and Proof-of-Concept environments.
• Strong ownership mindset and excellent problem-solving skills.
• Experience using AI-assisted development tools for coding, debugging, refactoring, and documentation.
• Experience with Google Cloud Platform.
• Hands-on experience with Vertex AI and Gemini.
• Experience with Vertex AI RAG Engine.
• Experience with AWS or Azure and willingness to work with GCP.
• Experience combining LLMs with Computer Vision, signal processing, or other multimodal AI systems.
• Experience fine-tuning foundation models or adapting open-source LLMs.
• Experience designing evaluation and benchmarking pipelines for AI systems.